Audience matching system for serving advertisements to displays

ABSTRACT

An audience matching system (“system”) maintains a plurality of audience polygons that enclose respective geographic regions and are associated with respective time periods, and respective target audiences. The system receives an advertisement request from a remote display system having a display at a particular geographic location, the request including an advertisement parameter that identifies a first target time period. The system selects a first audience polygon from the plurality of audience polygons, the selecting based in part on a geographic region associated with the first audience polygon enclosing the geographic location of the display and a time period associated with the first audience polygon being inclusive of the first target time period. The system determines a first target audience using the first audience polygon, and selects an advertisement associated with the first target audience. The system provides the advertisement to the remote display system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/001,238, filed May 21, 2014 and U.S. Provisional Application No.62/038,739, filed Aug. 18, 2014, which are both incorporated byreference in their entirety.

TECHNICAL FIELD

This disclosure relates generally to the advertisement field, and morespecifically to a new and useful display selection method in theadvertisement field.

BACKGROUND

Businesses generally advertise their products and/or services at or near(e.g., within 20 feet) their brick and mortar stores. However,businesses also would like to advertise to their customers (or potentialcustomers) at other locations, in a manner which has a high number of adimpressions with minimal cost. Conventionally, businesses identify theseother locations by looking for areas where large numbers of peoplecongregate (e.g., billboard at subway station). However, for a givenlocation, demographics of an audience tend to vary with time andbusinesses do not have efficient ways to account for these changes indemographics to ensure that advertisements are being presented to theintended audience.

SUMMARY

The above and other needs are met by a computer-implemented method, anon-transitory computer-readable storage medium storing executable code,and a device for matching advertisements to target audiences.

One embodiment of the computer-implemented method for matchingadvertisements to target audiences, comprises maintaining a plurality ofaudience polygons, wherein each audience polygon encloses a respectivegeographic region and is associated with a respective time period, and arespective target audience. An advertisement request is received from aremote display system having a display at a particular geographiclocation, the request including an advertisement parameter thatidentifies a first target time period. A first audience polygon isselected from the plurality of audience polygons, the selecting based inpart on a geographic region associated with the first audience polygonenclosing the geographic location of the display and a time periodassociated with the first audience polygon being inclusive of the firsttarget time period. A first target audience is determined using thefirst audience polygon, and an advertisement associated with the firsttarget audience is selected. The advertisement is provided to the remotedisplay system.

In another embodiment a non-transitory computer-readable storage mediumstoring executable computer program instructions for matchingadvertisements to target audiences, comprises maintaining a plurality ofaudience polygons, wherein each audience polygon encloses a respectivegeographic region and is associated with a respective time period, and arespective target audience. An advertisement request is received from aremote display system having a display at a particular geographiclocation, the request including an advertisement parameter thatidentifies a first target time period. A first audience polygon isselected from the plurality of audience polygons, the selecting based inpart on a geographic region associated with the first audience polygonenclosing the geographic location of the display and a time periodassociated with the first audience polygon being inclusive of the firsttarget time period. A first target audience is determined using thefirst audience polygon, and an advertisement associated with the firsttarget audience is selected. The advertisement is provided to the remotedisplay system.

In yet another embodiment an audience matching system, that comprises aprocessor configured to execute instructions stored on a non-transitorycomputer-readable storage medium. The instructions when executed by aprocessor, cause the system to maintain a plurality of audiencepolygons, wherein each audience polygon encloses a respective geographicregion and is associated with a respective time period, and a respectivetarget audience. The system receives an advertisement request from aremote display system having a display at a particular geographiclocation, the request including an advertisement parameter thatidentifies a first target time period. The system selects a firstaudience polygon from the plurality of audience polygons, the selectingbased in part on a geographic region associated with the first audiencepolygon enclosing the geographic location of the display and a timeperiod associated with the first audience polygon being inclusive of thefirst target time period. The system determines a first target audienceusing the first audience polygon, and selects an advertisementassociated with the first target audience. The system provides theadvertisement to the remote display system.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic representation of a system and method for servingadvertisements to displays, according to an embodiment.

FIG. 2 is a specific example of audience polygons within a geographicarea for a given time period.

FIG. 3A is a schematic representation of a sample dataset, according toan embodiment.

FIG. 3B is a schematic representation of a specific example ofdetermining which records satisfy a target audience condition, accordingto an embodiment.

FIG. 3C is a schematic representation of a specific example ofdetermining that device identifiers that have satisfied a targetaudience condition, according to an embodiment.

FIG. 3D is a schematic representation of a specific example of anaudience raster at a given time, according to an embodiment.

FIG. 3E is a schematic representation of a specific example of abaseline raster at the given time, according to an embodiment.

FIG. 3F is a schematic representation of a specific example of anormalized audience raster for the given time, according to anembodiment.

FIGS. 3G, 3H, 3J, and 3K are schematic representations of a specificexample of a variation of generating the audience polygon, according toan embodiment.

FIG. 3L is a schematic representation of selecting the audience polygonbased on the advertisement request, according to an embodiment.

FIG. 3M is a specific example of identifying the display devices withinthe audience polygon, according to an embodiment.

FIG. 3N is a schematic representation of sending the device identifiersfor the identified display devices to a remote system, according to anembodiment.

FIG. 4 is a schematic representation of recording a device identifier ata timestamp with a communication receiver, according to an embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The Figures (FIGS.) and the following description describe certainembodiments by way of illustration only. One skilled in the art willreadily recognize from the following description that alternativeembodiments of the structures and methods illustrated herein may beemployed without departing from the principles described herein.Reference will now be made in detail to several embodiments, examples ofwhich are illustrated in the accompanying figures. It is noted thatwherever practicable similar or like reference numbers may be used inthe figures and may indicate similar or like functionality.

FIG. 1 is a schematic representation of a system and method for servingadvertisements to displays, according to an embodiment. As shown in FIG.1, a method for serving advertisements to displays includes determining,by an audience matching system, audience polygons, wherein each audiencepolygon encloses a geographic region; receiving, by the audiencematching system, an advertisement request from a remote system (e.g.,advertisement exchange or advertiser); and selecting, by the audiencematching system, an audience polygon for the advertisement request.Conversely, the method includes: in response to receipt at the audiencematching system of an advertising request from a remote systemassociated with a display device, determining, by the audience matchingsystem, the set of audience polygon(s) that the display device islocated within; determining, by the audience matching system, one ormore advertisement requests that request any of the audiences associatedwith the set of audience polygons; and sending, by the audience matchingsystem, the advertisements associated with the determined advertisementrequests to the remote system. The method functions to use location datafor otherwise unknown users to determine when and where to displayadvertisements. The method functions to select an advertisement forpresentation on a display device, wherein the display device ispreferably a multi-user display device (e.g., a billboard), but canalternatively be a personal or single-user device (e.g., a mobiledevice), or any other suitable device (e.g., a display on a taxicab).The advertisement for presentation is preferably selected for anaudience wherein the interests of the specific users viewing theadvertisement are unknown, but can alternatively be selected for anaudience wherein the interests of the specific user(s) is known (e.g.,through a user profile or any other suitable user informationrepository).

As discussed below, an audience mapping system generates the audiencepolygons (also referred to as polygons). The generated audience polygonsfunction to determine the geographic regions in which any member of thetarget audience is likely to be located at a given time. FIG. 2 is aspecific example of audience polygons within a geographic area for agiven time period. The audience polygons are preferably pre-generated,but can alternatively be generated in real time, generated in responseto receipt of a new or updated dataset from a data source, periodicallyregenerated, checked, or updated, or generated at any other suitablefrequency. The audience polygons are preferably generated by theaudience matching system. In some embodiments, the audience matchingsystem also matches and/or serves the advertisements. In alternateembodiments, some or all of the functionality of the audience matchingsystem may be part of, e.g., a remote server or any other suitablecomputing system.

Each audience polygon is preferably associated with a geographic region,a target audience, and a time period. The audience polygon canadditionally be associated with a threshold affinity (e.g., probabilitythat people of the target audience will be within the polygon during thegiven time period). The geographic region is preferably the region inwhich the likelihood of a person satisfying the target audiencecondition is located is above a predetermined threshold, but canalternatively be any other suitable region. The target audience ispreferably defined by a target audience condition. The target audiencecondition is preferably a historical location or pattern of locations,wherein each member of the target audience was close to the historicallocation or historically exhibited the pattern of locations at leastonce. However, the target audience condition can be a keyword associatedwith a historical location or pattern of locations, or be any othersuitable filtering condition. A different audience polygon is preferablydetermined for each time period of a set of time periods. The timeperiod set is preferably a set of predetermined, recurring time periods,but can alternatively be a unique time period (e.g., have a specifictime and date, such as 4 pm-4:30 pm on 31 Mar. 2014). Each time periodis preferably an hour of a day of the week (e.g., between 2 PM-3 PM onMondays), but can alternatively have any other suitable interval (e.g.,be per month, per day, per minute, per second) and occur at any othersuitable frequency (e.g., daily, weekly, monthly, etc.). In onevariation of the method, the time period set includes each hour of theweek, such that 164 audience polygons are generated, one for each hourof the week. For example, the target audience condition can be“mothers,” wherein the historical location proximity used to filter thedevice data is “within 10 meters of a toy store,” the target audience islimited to devices that have been within 10 meters of a toy store, andthe audience polygon encloses a geographic region that has more than a75% probability that a mother (e.g., target audience members) will bewithin the geographic region at the given time period.

The dataset used to generate the audience polygons preferably includes aplurality of records (e.g., data points), wherein each record includesat least a unique identifier, a timestamp, and a geographic identifier.The dataset is preferably aggregated or received from cell serviceproviders, social networking systems (e.g., Facebook and Foursquare),mobile devices, or any other suitable source. The dataset canadditionally or alternatively be received from near-field communicationreceivers, such as Bluetooth, NFC, iBeacon interaction, WiFi hotspotinteraction, or other suitable communication receivers. For example,FIG. 4 is a schematic representation of recording a device identifier ata timestamp with a communication receiver, according to an embodiment.In operation, the communication receiver receives or detects a useridentifier, emitted or otherwise received from a user device (e.g.smartphone) associated with the user, when the user device is within athreshold radius of the receiver. The communication receivers arepreferably substantially static within a geographic location, but canalternatively be mobile. Data received from the communication receiverspreferably include a unique user identifier and a timestamp at which theuser identifier was received at the receiver, but can additionally oralternatively include other information. The unique identifierpreferably functions to uniquely identify a user or device, such as acell phone, tablet, or other mobile or mounted device. The uniqueidentifier can be encoded or unencoded. The unique identifier can be adevice identifier, a SIM card identifier, a phone number, anadvertisement identifier, or any other suitable (e.g., consistent)identifier. The unique identifier is preferably not directly associatedwith a set or source of user interests (e.g., a social networkingsystem, user profile, etc.) aside from the set of historical locationsassociated with the unique identifier within the dataset, such that theuser associated with the unique identifier is generally unknown.However, the unique identifier can be directly or indirectly associatedwith a user profile or identifier stored within the remote system, asocial networking system, or any other suitable system. The timestamppreferably includes a time, and can additionally include a date. Thegeographic identifier is preferably a set of latitude and longitudecoordinates, but can alternatively be a political region name (e.g., astate name, a city name, a region name, etc.) or be any other suitablename. The geographic identifier is preferably used as received from thedata source, but can alternatively be processed and made more accurate(e.g., such that the set of coordinates is closer to the true locationof the device or user).

As noted above, each record of the dataset includes a unique identifier,a timestamp, and a geographic identifier. In some embodiments, one ormore of the records may not include any personal identifying informationof a user, and instead, the audience matching system inferscharacteristics of the users by comparing the timestamps and geographicidentifiers to a set of rules to infer one or more characteristics ofusers of the devices. A rule is a set of geographic conditions, temporalconditions, or some combination thereof, that if met indicate one ormore characteristics of a user. The rules are provided by anadministrator of the audience matching system. For example, a first rulemight be that devices located at elementary schools between 7:00 am and8:30 am and/or between 3:00 pm and 4:30 pm during the work week indicatethat the users of the devices are parents. A second rule might be thatdevices located at elementary schools during the work week for more than3 hours are teachers or students. Additionally, combinations of rulesmay be used to further refine characteristics inferred about users. Forexample, a third rule that infers users are over 21 years of age iftheir associated devices are at a bar after 10:00 pm, combined with thesecond rule would indicate that the users are not students, but insteadwork at the school. Accordingly, by applying the rules to the recordsthe audience matching system is able to infer characteristics aboutusers of the devices without knowing the identity of the users.

The audience polygon is preferably generated by filtering the data(e.g., determined characteristics) with a target audience condition,segmenting the filtered data according to the set of time periods,generating an audience raster for the filtered data, and generating theaudience polygon from the audience raster. As used herein, a raster isrepresentative of a device distribution over a geographic area that hasbeen subdivided into a plurality of sub-regions. Additionally, thedevice distribution may be filtered by target audience condition and/ortime period. As discussed below with respect to FIG. 3E, a baselineraster is representative of the device distribution (e.g., all devicesthat there is a record of) over the geographic area for a given timeperiod. In contrast, an audience raster is representative of the devicedistribution that meets a particular targeting audience condition (e.g.,devices that are associated with users having an inferred characteristicthat matches the target audience condition) over the geographic area fora given time period. For example, as discussed below FIG. 3D shows anaudience raster at a particular time period RT4.

Filtering the data with a target audience condition functions toidentify the device identifiers that have satisfied the target audiencecondition at some point in time. The target audience condition ispreferably one of a set of predetermined target audience conditions, butcan alternatively be received from a third party (e.g., an advertiser).The target audience condition is preferably a threshold distance of agiven set of locations (e.g., within 10 meters of any coffee shop,within 10 meters of a coffee shop, etc.), but can alternatively be apattern of travel between a given set of locations (e.g., be within 20meters of a coffee shop and within 10 meters of a toy store in the spanof an hour), a keyword associated with a geographic proximity to a givenset of locations or pattern, or be any other suitable target audiencecondition. FIG. 3A is a schematic representation of a sample dataset,according to an embodiment. FIG. 3B is a schematic representation of aspecific example of determining which records satisfy the targetaudience condition, according to an embodiment. FIG. 3C is a schematicrepresentation of a specific example of determining that deviceidentifiers that have satisfied the target audience condition, accordingto an embodiment. Records satisfying the target audience conditions arepreferably identified, as shown in FIGS. 3A and 3B, and the associateddevice identifiers identified, as shown in FIG. 3C. The data set is thenfiltered using the identified device identifiers to extract all locationrecords that are associated with the identified device identifiers.

Segmenting the filtered data according to the set of time periodsfunctions to organize the filtered data by time, such that thedistribution of the devices across a geographical region for every giventime period can be determined. The time period is preferably the timeperiod for the audience polygon, but can alternatively be any othersuitable time period. The time period preferably has a time interval.The time interval (e.g., duration of the time period) is preferablyuniform (e.g., the same) for all time periods of the time period set,but can alternatively vary. The time interval is preferably an hour, butcan alternatively be a minute, several minutes, several hours, a day, orany other suitable time interval. The time interval is preferablypredetermined, but can alternatively be dynamically determined (e.g.,based on an advertisement request, based on the frequency ofadvertisement requests, etc.). The time period is preferably a recurringtime period, and can recur hourly (e.g., a minute of an hour), daily(e.g., an hour of the day), weekly (e.g., a week or hour of the week),monthly, yearly, biannually, or recur at any other suitable frequency.Examples of time periods can include seconds of a minute, seconds of anhour, seconds of a day, hours of a day, hours of the week (e.g.,recurring weekly), days of the week (e.g., recurring weekly), a day ofthe year, months, years (e.g., year in a decade), or any other suitablerecurring time period.

Generating an audience raster for the filtered data functions todetermine the distribution of audience members within a geographic areafor each given time period. The distribution of audience members ispreferably relative to a baseline (e.g., where the remainder of theaudience is), but can alternatively be relative to any other suitablereference value. The geographic area is preferably larger than andencompasses the geographic region associated with the audiencepolygon(s), but can alternatively be larger or smaller. The geographicarea preferably encompasses all locations referenced by the filtereddata, and more preferably encompasses all locations referenced by thedataset. However, the geographic area can only cover a subset of alllocations referenced by the filtered data or the dataset. The geographicarea is preferably the entire world (e.g., the surface of the planet),but can alternatively be a continent, a state, a city, or any othersuitable politically or physically defined area. The audience rasterpreferably includes a set of geographic sub-regions and a number ofaudience members within each sub-region, but can additionally oralternatively be associated with any other suitable audience parameter.The audience raster is preferably generated using the filtered data, butcan alternatively be generated using any other suitable data. Anaudience raster is preferably generated for each time period, but canalternatively be generated for the filtered data overall. The audienceraster is preferably two-dimensional, but can alternatively bethree-dimensional (e.g., accommodate for the geographic topology), orhave any other suitable number of dimensions.

Generating the audience raster for a time period preferably includesdividing the geographic area into segments, mapping the filtered datafor the time period to the geographic area, and determining the numberof records within each segment, as shown in FIG. 3D which is a schematicrepresentation of a specific example of an audience raster at a giventime, according to an embodiment.

The segments of the raster are preferably discretized, non-overlapping,adjacent segments that each encompasses a geographic sub-region of thegeographic area, wherein the sub-regions are preferably the sub-regionsthat the audience raster is associated with. The set of segmentspreferably cooperatively encompass the entirety of the geographic area,but can alternatively encompass a subset of the geographic area. Thesegments or sub-regions of the set are preferably uniform and have thesame shape, encompass the same amount of geographic area, or encompassthe same number of unique latitude and longitude coordinates. However,the segments of the raster can alternatively be overlapping,non-uniform, or have any other suitable property. The sizes of thesegments are preferably predetermined, but can alternatively beautomatically determined based on the number of records (e.g., overallor for the given time period), based on the desired resolution (e.g.,meters or kilometers), or determined in any other suitable manner. Forexample, generating the segments of the raster can include dividing thegeographic area into an array, or overlaying an array on the geographicarea.

Mapping the filtered data for the time period to the geographic areaincludes mapping the device identifier or a representation thereof ontothe segmented geographic area based on the location identifier for therecord satisfying the time period. Different maps are preferably createdfor different time periods. Because the filtered data only includes thelocation records for device identifiers that had satisfied the targetaudience condition at some point in time (e.g., not necessarily themapped record), mapping the filtered data for the time period results inthe audience distribution across the geographic region for the giventime period. The filtered data can be mapped before geographic areasegmentation, after geographic area segmentation, or mapped at any othersuitable time. For example, all filtered data records having a timestampcorresponding to the time period of 2 pm-3 pm on Tuesdays can be mappedto a first map (irrespective of month or year), while all filtered datarecords having a timestamp corresponding to the time period of 3 pm-4 pmon Tuesdays can be mapped to a second map (irrespective of month oryear). Mapping the filtered data can additionally include filtering therecords for each segment to eliminate redundant device identifiers(e.g., when the same device identifier appears in the segment multipletimes for the given time period). However, the mapped filtered data canbe otherwise processed.

Determining the number of audience records within each segment functionsto determine the number of audience members or device identifiers ineach segment. The number of records within each segment is preferablycounted, but can be otherwise determined. The density of the audiencemembers (e.g., number of audience members per geographic unit) or anyother suitable audience location parameter can additionally bedetermined. For recurring time periods, the number of audience recordswithin each segment can be determined by determining the mean, median,maximum, or any other suitable measure of the number of audience recordsacross all instances of the time period. However, the number of audiencerecords can be otherwise determined.

Determining the number of records within each segment can additionallyinclude determining the maximum and minimum number of records within thesegment for a given time period. This can be particularly applicablewhen the time periods are recurring. The maximum and minimum number ofrecords can be determined across all instances of the given time period,determined across different instances of the time period, or determinedin any suitable manner. For example, the maximum and minimum number ofrecords can be determined for the general time period of 2 pm-3 pm onTuesdays, wherein the maximum number of records (in aggregate, acrossall records of the time period) occurs at 2:14 pm, and the minimumnumber of records (in aggregate, across all records of the time period)occurs at 2:44 pm. In another example, the maximum and minimum number ofrecords can be determined based on the individual instances having themaximum and minimum numbers. For example, the minimum number of recordscan be determined from a first instance of the time period of 2 pm-3 pmon Tuesdays occurring on 1 Apr. 2014, and the maximum number of recordscan be determined from a second instance of the time period of 2 pm-3 pmon Tuesdays occurring on 18 Mar. 2014.

Generating the audience polygon from the audience raster functions todelineate a geographic area in which the audience members are likely(e.g., beyond a threshold percentage likelihood) to be located within atthe given time period.

In a first variation, the audience polygon is generated based on theaudience member density, wherein generating the audience polygon caninclude determining the physical geographic boundaries that would resultin a predetermined density of audience members. Physical geographicboundaries can include geographic features, such as mountain ranges,watercourses, coastlines, or any other suitable geographic feature.Physical geographic boundaries can additionally include manmadefeatures, such as walls, gates, roads, or any other suitable man madebarrier. The audience polygon can be automatically iterativelydetermined. For example, iteratively determining the audience polygoncan include identifying a cluster of audience raster segments havingaudience member densities (or audience member numbers) above apredetermined threshold, identifying the physical geographic boundariesencircling the identified raster segment cluster, rasterizing the areaencompassed within the physical geographic boundary, and selecting asecond set of substantially continuous physical geographic boundariesthat better encircle the desired density of audience members. Thedesired density (e.g., threshold density or threshold number) ofaudience members is preferably predetermined, but can alternatively bebased on the advertisement request or determined in any other suitablemanner. In the former instance, different audience polygons can bedetermined for the same geographic sub-region for the same time period,wherein each of the audience polygons is associated with a differentthreshold density level. For example, a first, second, and thirdaudience polygon can be determined for a given geographic region, giventime period, and given target audience, wherein the first audiencepolygon encircles a high audience density (e.g., 10 audience members persquare meter), the second audience polygon encircles a medium audiencedensity (e.g., 5 audience members per square meter), and the thirdaudience polygon encircles a low audience density (e.g., 1 audiencemember per kilometer). The second audience polygon preferablyencompasses the first audience polygon, and the third audience polygonpreferably encompasses the second audience polygon, but the first,second, and third audience polygons can alternatively each coverdifferent geographic points. Alternatively, the physical geographicboundaries can be manually determined or determined any other suitablemanner.

In a second variation of the method, generating the audience polygonincludes generating a set of line segments about a geographic areahaving a probability above a threshold probability of an audience memberbeing located within the geographic area, merging the line segments, andjoining the line segments. The resultant audience polygon is preferablyone that encompasses (e.g., encircles or surrounds) a geographic area inwhich the probability that an audience member will be located within thegeographic area at the given time is above a threshold probability.

Generating the line segments preferably includes determining theprobability that an audience member is within a geographic segment,relative to the rest of the population, as shown in FIG. 3F which is aschematic representation of a specific example of a normalized audienceraster for the given time, according to an embodiment; and generatingline segments based on the probability that the audience member iswithin the raster segment.

Determining the probability that an audience member is within ageographic segment preferably includes normalizing the number ofaudience members within a segment of the audience raster with anormalization value, but the probability can be determined in any othersuitable manner. Alternatively, this can include determining how muchthe concentration of the audience within the geographic segment variesfrom the reference value. Alternatively, this can include dividing theaudience raster by the baseline to obtain an actual probability of anysample being in the audience. One advantage of this embodiment is thatthere may be a lot of noise in both the baseline raster and the audienceraster, and the division of the audience raster by the baselineeffectively divides out the noise providing a relatively cleannormalized audience raster. All or a portion of the calculatedprobabilities can be verified against a second data source (e.g., aNielson number). The normalization value is preferably determined fromthe entire dataset, but can alternatively be determined from a subset ofthe dataset, predetermined, or determined in any other suitable manner.In one variation of the method, the normalization value is determinedbased on a baseline raster, as shown in FIG. 3E which is a schematicrepresentation of a specific example of a baseline raster at the giventime, according to an embodiment. A baseline raster is representative ofthe device distribution (e.g., all devices that there is a record of)over the geographic area that is subdivided into a plurality ofsub-regions for a given time period. The baseline raster is preferablygenerated using all the data points of the dataset satisfying the timeperiod, but can alternatively be generated using the entirety of thedataset, or any suitable portion of the dataset. The time period ispreferably the same as that used to generate the audience raster but canalternatively be any other suitable time period. The baseline raster ispreferably generated by dividing the geographic area (preferably thesame geographic area as that used to generate the audience raster,alternatively a different geographic area) into discrete,non-overlapping raster segments (preferably the same segments as that ofthe audience raster but alternatively different segments), eachassociated with a geographic sub-region, that cooperatively cover theentirety of the geographic area. Alternatively, any other suitableraster segments can be used. The records satisfying the time period arethen mapped to the geographic location indicated by the respectivelocation identifier, similar to mapping the filtered data for the timeperiod to the geographic area. The number of records within eachbaseline segment (e.g., within each sub-region) is preferablydetermined, similar to determining the number of audience records withineach segment. The maximum and minimum number of baseline records withineach baseline segment can additionally be determined, similar todetermining the maximum and minimum number of audience records in eachaudience segment. The normalization value is preferably determined bydividing the maximum number of baseline records within a first rastersegment (for a first time period) by the maximum number of audiencerecords within the first raster segment (for the first time period). Theprobability that an audience member is within the first raster segmentis preferably determined by multiplying the number of audience recordsin the first raster segment for the first time period with thenormalization value and dividing the product by the baseline value forthe first raster segment for the first time period. However, theprobability that an audience member is within the first raster segmentcan be otherwise determined.

Generating the line segment based on the probability that the audiencemember is within the raster segment (audience probability) preferablyincludes generating a line segment for each geographic segment of theaudience raster, but the line segments can be generated in any othersuitable manner. Line segments are preferably generated within or alongthe audience raster segments that fall between a first geographicsegment having an audience probability above a first probabilitythreshold and a second geographic segment having an audience probabilitybelow a second probability threshold, or shares a boundary with ageographic segment having an audience probability above the firstprobability threshold or below the second probability threshold. Thefirst probability threshold is preferably equivalent to the secondprobability threshold, but can alternatively be different. One or moresets of first and/or second probability thresholds can be used, whereinmultiple audience polygons (e.g., sets of line segments) can bedetermined for each audience raster, wherein each audience polygon ispreferably associated with a different probability that an audiencemember will be within the audience polygon (audience affinity). Thenumber of sets and the thresholds can be predetermined, dynamicallydetermined based on the advertisement request (e.g., extracted from theadvertisement request), or determined in any other suitable manner. Forexample, if four thresholds are selected: above 0%, above 25%, above50%, and above 75%, the method preferably generates the following setsof audience polygons, respectively: a first set of audience polygonssurrounding geographic regions wherein more than 0% of the populationsatisfied the target audience condition, a second set of audiencepolygons surrounding geographic regions wherein more than 25% of thepopulation satisfied the target audience condition, a third set ofaudience polygons surrounding geographic regions wherein more than 50%of the population satisfied the target audience condition, and a fourthset of audience polygons surrounding geographic regions wherein morethan 75% of the population satisfied the target audience condition.However, the line segments can be generated based on any other suitableparameter The line segments are preferably generated for the audienceraster segments using a marching squares method based on whether or noteach audience raster segment satisfies the applied probabilitythreshold, as shown in FIGS. 3G-3K which are schematic representationsof a specific example of a variation of generating the audience polygon,according to an embodiment, but the line segments can alternatively begenerated using any other suitable method. In one specific example, forgiven audience raster segment centered about the latitude and longitudecoordinates of [39.59, −75.15] for a first time period, the baselinevalue is 553 unique devices, the audience value is 12 devices associatedwith the target audience condition, the normalization vector is 15.7,and the probability that a member of the audience is within the audienceraster segment is 0.37%. The audience raster segment would be includedwithin the geographic region enclosed by an audience polygon for thetarget audience condition and time period having a threshold probability(affinity) of 0%, would be included within the geographic regionenclosed by an audience polygon for the target audience condition andtime period having a threshold probability (affinity) of 25%, would notbe included within the geographic region enclosed by an audience polygonfor the target audience condition and time period having a thresholdprobability (affinity) of 50%, and would not be included within thegeographic region enclosed by an audience polygon for the targetaudience condition and time period having a threshold probability(affinity) of 75%.

The line segments are preferably merged by combining the line segments,such as by using MapReduce's reduce function. However, contiguous orproximal line segments can be otherwise merged.

The merged line segments are preferably subsequently joined, such as byusing MapReduce's reduce function. However, contiguous or proximalmerged line segments can be otherwise joined. The resultant audiencepolygons are preferably then indexed and stored for advertisementquerying, as shown in FIG. 3L which is a schematic representation ofselecting the audience polygon based on the advertisement request,according to an embodiment.

Receiving an advertisement request from a remote system (e.g.,advertisement exchange or advertiser) functions to trigger a systemaudience polygon query. The remote system may be part of an advertiser.In some embodiments, an advertiser may refer to an advertisementexchange. Receipt of an advertisement request can additionally oralternatively trigger audience polygon generation. The advertisementrequest preferably includes an audience parameter, a desired affinity,and an advertisement time. However, the advertisement request canadditionally include other advertisement parameters, such as a desiredgeographic area or advertisement frequency, only include the audienceparameter, or include any other suitable combination of audienceparameters. The audience parameter can be a unique location (e.g., alatitude longitude coordinate), a venue category (e.g., coffee shops), ageographic distance from a venue category (e.g., within 10 meters of acoffee shop), a pattern of venues or unique locations, a persona orkeyword, wherein the persona or keyword is associated with a geographicidentifier such as a unique location or venue identifier by the audiencematching system or advertiser (e.g., “mothers of young children,”wherein the persona is associated with a pattern of visiting a coffeeshop then a toy store within the same hour), or any be any othersuitable audience parameter directly or indirectly associated with ageographic identifier. The probability received is preferably one of thesets of probability thresholds used to determine the audience polygons,but can alternatively be any other suitable probability, wherein theaudience polygon for the received probability can be dynamicallygenerated, or the audience polygon for the probability threshold higherthan, lower than, or closest to the received probability can be used.For example, an advertisement request can include people who have beenwithin 10 meters of a coffee shop, an 80% affinity, and a 3 pm timeperiod, wherein the advertisement is to be shown at 3 pm in areas havingmore than 80% probability that someone who has been within 10 meters ofany coffee shop. Alternatively, when an advertisement is received fromthe remote system, the audience matching system can extract one or moreadvertisement parameters, such as the time period, affinity, and/ortarget audience from the advertisement content (e.g., through keywordanalysis, image analysis, cadence analysis, volume analysis, etc.).

Selecting an audience polygon for the advertisement request functions todetermine the geographic region in which a display device should beselected, as shown in FIG. 3L. The audience polygon that is selected ispreferably for the audience parameter specified in the advertisementrequest, or for a target audience condition that is associated with theaudience parameter (e.g., through natural language hierarchies, keywordassociation, etc.). The audience polygon that is selected preferablyadditionally satisfies the requested time period, wherein the audiencepolygon is preferably associated with a polygon time period that is thesame as the requested time period, overlaps with the requested timeperiod, or is encompassed within the requested time period. The audiencepolygon that is selected preferably additionally satisfies the requestedaffinity, wherein the audience polygon preferably encompasses a regionhaving an audience affinity that matches or is higher than the requestedaffinity. However, the audience polygon can alternatively encompass aregion having an audience affinity that is lower than the requestedaffinity. However, any other suitable set of audience polygons can beselected. A single audience polygon that best satisfies the requestedparameters can be selected, or a set of audience polygons can beselected.

The method can additionally include identifying the display systemidentifier for a display physically located within the geographic regionwithin the set of selected audience polygons, as shown in FIG. 3M whichis a specific example of identifying the display devices within theaudience polygon, according to an embodiment. The audience is preferablydetermined in response to a request to display the advertisement (e.g.,at an advertisement display time), but can alternatively bepredetermined for each advertisement. The display device is preferablystatically mounted to a location within the geographic region, but canalternatively be mobile. The display device is preferably a publicdisplay, and is preferably capable of presenting an advertisement or anyother suitable content to multiple users. The display is preferably adigital billboard, but can alternatively be a digital poster, array oftelevisions, or any other suitable statically mounted, multi-userdisplay. However, the display can be the display of a personal mobiledevice, a vehicle, a static personal device, or any other suitabledisplay. The display is preferably associated with a processing systemthat receives content, schedules content, and controls content renderingon the display. The processing system can additionally track theidentifier of the content that is presented on the display, thefrequency or number of times the content is presented, or any othersuitable content parameter. Each display system can include one or moredisplays. The audience matching system can select identifiers for entiredisplay systems, wherein a portion or the entirety of the displaydevices associated with the display system are located within thegeographic region of the audience polygon, or select identifiers forindividual display devices, wherein the individual display devices arepreferably located within the geographic region of the audience polygon.However, any other suitable display device identifier or systemidentifier can be selected. The device location, device identifier, orsystem identifier is preferably sent to the remote server from which theadvertisement request was received, as shown in FIG. 3N which is aschematic representation of sending the device identifiers for theidentified display devices to a remote system, according to anembodiment. Alternatively, the information sent to the remote system caninclude the geographic locations of the display devices within theadvertisement polygons, the display identifiers of the display deviceswithin the advertisement polygons, the system identifiers (e.g., virtualaddresses) associated with the identified display devices, or any othersuitable information to the remote system. Alternatively, anadvertisement can additionally be received in conjunction with theadvertisement request, wherein the audience matching system can send theadvertisement to the display devices located within the audiencepolygons that meet the advertisement request parameters. Theadvertisement or information is preferably wirelessly transmitted to therecipient, but can alternatively be transferred over a wired connection.

The method can additionally include scheduling advertisements, whereinthe information sent to the remote server or display system preferablyfurther includes a set of times for advertisement display. In onevariation of the method, the advertisements or advertisement requestscan have different priorities for a given audience polygon, wherein highpriority advertisements can be shown first, shown longer, shown athigher frequencies, or have any other suitable presentation parameter.The priorities can be determined based on the bid price associated withthe advertisement, the priority for the advertisement, received as partof the advertisement request, the order in which the associatedadvertisement request was received, the percentage match of the audienceregion or anticipated users within the audience region with theadvertisement request, or determined in any other suitable manner.

The method can additionally include receiving confirmation ofadvertisement presentation. The confirmation can be a confirmation ofadvertisement display within the geographic region associated with theselected audience, confirmation of advertisement presentation on adisplay system located within the geographic region (e.g., a displayidentified by the audience matching system), or any other suitableconfirmation. The confirmation can additionally include the number oftimes the advertisement was displayed, or any other suitablepresentation parameter. The confirmation can be subsequently used tobill the advertiser. In response to receipt of payment from theadvertiser, portions of the payment can be disbursed to the audiencematching systems associated with the display devices based on theadvertisement presentation duration, frequency, number of times, timeperiod, or any other suitable advertisement display parameter. However,the confirmation can be otherwise used. The method can additionally oralternatively include any suitable combination of the aforementionedelements.

An alternative embodiment preferably implements the above methods in acomputer-readable medium storing computer-readable instructions. Theinstructions are preferably executed by computer-executable componentspreferably integrated with an advertisement routing system. Theadvertising routing system may include an audience matching system. Theaudience matching system may include an audience polygon generationsystem that functions to generate one or more geofences enclosinggeographic areas in which people satisfying a historic geographiccondition have a high likelihood of being during a given time period, anadvertisement request matching system that functions to matchadvertisement requests with a set of audience polygons, and an audiencepolygon reply system that functions to send an advertiser oradvertisement exchange the locations, addresses, or identifiers ofdisplay systems within the region bounded by the selected audiencepolygons. The computer-readable medium may be stored on any suitablecomputer readable media such as RAMs, ROMs, flash memory, EEPROMs,optical devices (CD or DVD), hard drives, floppy drives, or any suitabledevice. The computer-executable component is preferably a processor butthe instructions may alternatively or additionally be executed by anysuitable dedicated hardware device.

An alternative embodiment preferably implements the above methods in anon-transitory computer-readable medium storing computer-readableinstructions. The instructions are preferably executed bycomputer-executable components preferably integrated with a locationcorrection system as described in detail in U.S. application Ser. No.14/688,756, filed on Apr. 16, 2015, and which is hereby incorporated byreference in its entirety. The location correction system can include acell region mapping system, probability determination system, andlocation selection system. The computer-readable medium may be stored onany suitable computer readable media such as RAMs, ROMs, flash memory,EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or anysuitable device. The computer-executable component is preferably aprocessor but the instructions may alternatively or additionally beexecuted by any suitable dedicated hardware device.

Some portions of the above description describe the embodiments in termsof algorithmic processes or operations. These algorithmic descriptionsand representations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs comprising instructions for executionby a processor or equivalent electrical circuits, microcode, or thelike. Furthermore, it has also proven convenient at times, to refer tothese arrangements of functional operations as modules, without loss ofgenerality. The described operations and their associated modules may beembodied in software, firmware, hardware, or any combinations thereof.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. It should be understood thatthese terms are not intended as synonyms for each other. For example,some embodiments may be described using the term “connected” to indicatethat two or more elements are in direct physical or electrical contactwith each other. In another example, some embodiments may be describedusing the term “coupled” to indicate that two or more elements are indirect physical or electrical contact. The term “coupled,” however, mayalso mean that two or more elements are not in direct contact with eachother, but yet still co-operate or interact with each other. Theembodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary.“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the disclosure. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs formatching advertisements to audiences. Thus, while particular embodimentsand applications have been illustrated and described, it is to beunderstood that the described subject matter is not limited to theprecise construction and components disclosed herein and that variousmodifications, changes and variations which will be apparent to thoseskilled in the art may be made in the arrangement, operation and detailsdisclosed herein.

The invention claimed is:
 1. A computer-implemented method for matchingadvertisements to target audiences, comprising: receiving a targetaudience condition wherein the target audience condition includes ageographic location and a time period associated with a target audience;determining the target audience based on the target audience condition,the target audience comprising one or more audience members; receivingan advertisement request from a remote display system having a displayat a particular geographic location, the request including anadvertisement parameter that identifies a first target time period;filtering an aggregated dataset based on the received target audiencecondition; segmenting the filtered data according to a set of timeperiods; generating an audience raster based on the filtered datawherein the audience raster is representative of a distribution of aplurality of mobile devices that satisfy the received target audiencecondition over a plurality of sub-regions of the geographic area for agiven one of the set of time periods; generating, based on the audienceraster, a first audience polygon based on a set of line segmentsenclosing a geographic area wherein the one or more audience membershave a probability above a threshold probability of being located withinthe geographic area; selecting an advertisement associated with thetarget audience; and providing the selected advertisement to the remotedisplay system for presentation on the display at the particulargeographic location during the first target time period.
 2. TheComputer-implemented method of claim 1, further comprising: receiving adata set including a plurality of records from one or more cell serviceproviders that are associated with mobile device users, each recordincluding a respective timestamp and geographic identifier; inferring aplurality of characteristics about one or more of the mobile deviceusers by comparing timestamps and geographic identifiers in each recordto a set of rules that map inferred characteristics to specificcombinations of geographic and temporal conditions; and generating oneor more audience polygons using the inferred plurality ofcharacteristics.
 3. The computer-implemented method of claim 2, whereingenerating one or more audience polygons using the inferred plurality ofcharacteristics, comprises: determining a baseline group of records inthe data set having timestamps within a first time period; generating abaseline raster using the baseline group of records, the baseline rasterdescribing a baseline distribution of mobile device users across ageographic region which is subdivided into a plurality of sub-regions;determining a subset of the baseline group of records that areassociated with mobile device users that each have in common a firstinferred characteristic of the inferred plurality of characteristics;generating a second audience raster using the subset of the baselinegroup of records, the second audience raster describing an audiencedistribution across the plurality of sub-regions for the mobile deviceusers associated with the subset of the baseline group of records;generating a normalized audience raster using the audience raster andthe baseline raster, the normalized audience raster describing anormalized audience distribution across the plurality of sub-regions andindicating affinities describing probabilities that mobile device usershaving the first characteristic are found within each of the pluralityof sub-regions; and generating the one or more audience polygons usingthe normalized audience raster.
 4. The computer-implemented method ofclaim 3, wherein generating the one or more audience polygons using thenormalized audience raster comprises: identifying regions of theplurality of regions of the normalized audience raster that havedifferent affinities; generating the one or more audience polygons usingthe identified regions, each audience polygon associated with the firsttime period and one of the different affinities; and storing the one ormore audience polygons as part of the plurality of audience polygons. 5.The computer-implemented method of claim 1, wherein the display isstatically mounted at the particular geographic location and isconfigured to present the advertisement to multiple members of thetarget audience.
 6. The computer-implemented method of claim 1, whereingenerating a first audience polygon based on a set of line segmentscomprises: determining the probability that the audience member iswithin a geographic segment associated with the first audience polygonrelative to a population of audience members; and generating the set ofline segments based on the probability that the audience member iswithin the geographic region associated with the set of line segments.7. The computer-implemented method of claim 6, wherein determining theprobability that the audience member is within the geographic locationcomprises normalizing a number of audience members within a segment ofthe audience raster with a normalization value.
 8. A non-transitorycomputer-readable storage medium storing executable computer programinstructions for method for matching advertisements to target audiences,the instructions executable to perform steps comprising: receiving atarget audience condition wherein the target audience condition includesa geographic location and a time period associated with a targetaudience; determining the target audience based on the target audiencecondition, the target audience comprising one or more audience members;receiving an advertisement request from a remote display system having adisplay at a particular geographic location, the request including anadvertisement parameter that identifies a first target time period;filtering an aggregated dataset based on the received target audiencecondition; segmenting the filtered data according to a set of timeperiods; generating an audience raster based on the filtered datawherein the audience raster is representative of a distribution of aplurality of mobile devices that satisfy the received target audiencecondition over a plurality of sub-regions of the geographic area for agiven one of the set of time periods; generating, based on the audienceraster, a first audience polygon based on a set of line segmentsenclosing a geographic area wherein the one or more audience membershave a probability above a threshold probability of being located withinthe geographic area; selecting an advertisement associated with thetarget audience; and providing the selected advertisement to the remotedisplay system for presentation on the display at the particulargeographic location during the first target time period.
 9. Thecomputer-readable medium of claim 8, further comprising; receiving adata set including a plurality of records from one or more cell serviceproviders that are associated with mobile device users, each recordincluding a respective timestamp and geographic identifier; inferring aplurality of characteristics about one or more of the mobile deviceusers by comparing timestamps and geographic identifiers in each recordto a set of rules that map inferred characteristics to specificcombinations of geographic and temporal conditions; and generating oneor more audience polygons using the inferred plurality ofcharacteristics.
 10. The computer-readable medium of claim 9, whereingenerating one or more audience polygons using the inferred plurality ofcharacteristics, comprises: determining a baseline group of records inthe data set having timestamps within a first time period; generating abaseline raster using the baseline group of records, the baseline rasterdescribing a baseline distribution of mobile device users across ageographic region which is subdivided into a plurality of sub-regions;determining a subset of the baseline group of records that areassociated with mobile device users that each have in common a firstinferred characteristic of the inferred plurality of characteristics;generating a second audience raster using the subset of the baselinegroup of records, the second audience raster describing an audiencedistribution across the plurality of sub-regions for the mobile deviceusers associated with the subset of the baseline group of records;generating a normalized audience raster using the second audience rasterand the baseline raster, the normalized audience raster describing anormalized audience distribution across the plurality of sub-regions andindicating affinities describing probabilities that mobile device usershaving the first characteristic are found within each of the pluralityof sub-regions; and generating the one or more audience polygons usingthe normalized audience raster.
 11. The computer-readable medium ofclaim 10, wherein generating the one or more audience polygons using thenormalized audience raster comprises: identifying regions of theplurality of regions of the normalized audience raster that havedifferent affinities; generating the one or more audience polygons usingthe identified regions, each audience polygon associated with the firsttime period and one of the different affinities; and storing the one ormore audience polygons as part of the plurality of audience polygons.12. The computer-readable medium of claim 8, wherein generating a firstaudience polygon based on a set of line segments comprises: determiningthe probability that the audience member is within a geographic segmentassociated with the first audience polygon relative to a population ofaudience members; and generating the set of line segments based on theprobability that the audience member is within the geographic regionassociated with the set of line segments.
 13. The computer-readablemedium of claim 12, wherein determining the probability that theaudience member is within the geographic location comprises normalizinga number of audience members within a segment of the audience rasterwith a normalization value.
 14. An audience matching system comprising:a processor; and a non-transitory computer-readable storage mediumcoupled to the processor, the computer-readable storage medium includinginstructions that, when executed by the processor, cause the system toperform steps comprising: receiving a target audience condition whereinthe target audience condition includes a geographic location and a timeperiod associated with a target audience; determining the targetaudience based on the target audience condition, the target audiencecomprising one or more audience members; receiving an advertisementrequest from a remote display system having a display at a particulargeographic location, the request including an advertisement parameterthat identifies a first target time period; filtering an aggregateddataset based on the received target audience condition; segmenting thefiltered data according to a set of time periods; generating an audienceraster based on the filtered data wherein the audience raster isrepresentative of a distribution of a plurality of mobile devices thatsatisfy the received target audience condition over a plurality ofsub-regions of the geographic area for a given one of the set of timeperiods; generating, based on the audience raster, a first audiencepolygon based on a set of line segments enclosing a geographic areawherein the one or more audience members have a probability above athreshold probability of being located within the geographic area;selecting an advertisement associated with the target audience; andproviding the selected advertisement to the remote display system forpresentation on the display at the particular geographic location duringthe first target time period.
 15. The system of claim 14, wherein theinstructions, when executed by the processor, further cause the systemto perform steps comprising: receiving a data set including a pluralityof records from one or more cell service providers that are associatedwith mobile device users, each record including a respective timestampand geographic identifier; inferring a plurality of characteristicsabout one or more of the mobile device users by comparing timestamps andgeographic identifiers in each record to a set of rules that mapinferred characteristics to specific combinations of geographic andtemporal conditions; and generating one or more audience polygons usingthe inferred plurality of characteristics.
 16. The system of claim 15,wherein generating one or more audience polygons using the inferredplurality of characteristics, comprises: determining a baseline group ofrecords in the data set having timestamps within a first time period;generating a baseline raster using the baseline group of records, thebaseline raster describing a baseline distribution of mobile deviceusers across a geographic region which is subdivided into a plurality ofsub-regions; determining a subset of the baseline group of records thatare associated with mobile device users that each have in common a firstinferred characteristic of the inferred plurality of characteristics;generating a second audience raster using the subset of the baselinegroup of records, the second audience raster describing an audiencedistribution across the plurality of sub-regions for the mobile deviceusers associated with the subset of the baseline group of records;generating a normalized audience raster using the second audience rasterand the baseline raster, the normalized audience raster describing anormalized audience distribution across the plurality of sub-regions andindicating affinities describing probabilities that mobile device usershaving the first characteristic are found within each of the pluralityof sub-regions; and generating the one or more audience polygons usingthe normalized audience raster.
 17. The system of claim 16, whereingenerating the one or more audience polygons using the normalizedaudience raster comprises: identifying regions of the plurality ofregions of the normalized audience raster that have differentaffinities; generating the one or more audience polygons using theidentified regions, each audience polygon associated with the first timeperiod and one of the different affinities; and storing the one or moreaudience polygons as part of the plurality of audience polygons.
 18. Thesystem of claim 14, wherein the display is statically mounted at theparticular geographic location and is configured to present theadvertisement to multiple members of the target audience.
 19. The systemof claim 14, wherein generating a first audience polygon based on a setof line segments comprises: determining the probability that theaudience member is within a geographic segment associated with the firstaudience polygon relative to a population of audience members; andgenerating the set of line segments based the probability that theaudience member is within the geographic region associated with the setof line segments.
 20. The system of claim 19, wherein determining theprobability that the audience member is within the geographic locationcomprises normalizing a number of audience members within a segment ofthe audience raster with a normalization value.